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Found 9 papers, 1 papers with code
Seeing the World through Text: Evaluating Image Descriptions for Commonsense Reasoning in Machine Reading Comprehension
Despite recent achievements in natural language understanding, reasoning over commonsense knowledge still represents a big challenge to AI systems.
Resource of Wikipedias in 31 Languages Categorized into Fine-Grained Named Entities
The training data were provided by Japanese categorization and the language links, and the task was to categorize the Wikipedia pages into 30 languages, with no language links from Japanese Wikipedia (20M pages in total).
An Empirical Study of Personalized Federated Learning
Federated learning is a distributed machine learning approach in which a single server and multiple clients collaboratively build machine learning models without sharing datasets on clients.
FedMe: Federated Learning via Model Exchange
First, to optimize the model architectures for local data, clients tune their own personalized models by comparing to exchanged models and picking the one that yields the best performance.
SHINRA2020-ML: Categorizing 30-language Wikipedia into fine-grained NE based on "``Resource by Collaborative Contribution" scheme
In our "Resource by Collaborative Contribution (RbCC)" scheme, we conducted a shared task of structuring Wikipedia to attract participants but simultaneously submitted results are used to construct a knowledge base.
Investigating the Challenges of Temporal Relation Extraction from Clinical Text
Temporal reasoning remains as an unsolved task for Natural Language Processing (NLP), particularly demonstrated in the clinical domain.
